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Geoscientists: Come get skilled up in scientific computing and machine learning in time for the new year!

This two-day course is delivered by alternating short tutorials with hands-on programming exercises. Participants work on their laptops in a small and friendly classroom setting. You'll receive the entire set of course materials in the form of a digital interactive coding notebooks before the course starts, and you'll create several programs from scratch by the end of the course.

The course uses real industry data such as logs, horizons, seismic, and GIS data — making it particularly relevant to geoscientists. However, the subject matter we cover is broad enough to be useful to the full range of technical disciplines in the energy and natural resources industry.

We teach using the Python programming language. Python is the fastest growing language for scientific computing. It’s easy-to-learn, powerful, and has an ecosystem of thousands of open source code libraries. And it’s free, so you’re not stuck with awkward licensing concerns. Come see what all the fuss is about.

DAY 1 — Intro to the scientific computing with Python

On the first day, we will get used to Python's syntax and built-in functions, and explore the rich world of NumPy and SciPy. At the end of the day, we will have a good overview of the basic toolset used by earth scientists everywhere, and be ready to dive deeper. The learning objectives for day 1 include:

Demystifying the installation process and getting a computing environment setup on your laptop.

On day 2, we'll explore the Pandas and Scikit Learn packages for machine learning tasks using geoscience data examples. After this day, students will have a good overview of how to look at large data sets and solve problems with state-of-the-art machine learning tools. The learning objectives for day 2 include:

Practice data wrangling in Pandas.

Describe the difference between supervised and unsupervised machine learning methods.

What does a model mean in the context of machine learning? How do we choose and parameterize models in machine learning?

How to assess model results and performance metrics. Ways of improving performance